Technology for converting a color image into a grayscale image is mainly used in printing color images with a black and white printing apparatus. In addition, the above-mentioned color to grayscale image conversion technology may be used to generate an intermediate image required for the application of various image processing techniques, such as edge detection, and image enhancement, and various computer vision techniques, such as object tracking.
In general, luminance Y of a color has been mainly used in a process of converting a color image into a grayscale image. As for the color's luminance, National Television System Committee (NTSC) Y or Commission Internationale de L'Eclairage (CIE) Y defined in the CIE XYZ color space may be exemplified.
In a method of converting a color image into a grayscale image using luminance, it is possible to accurately represent independent brightness of each pixel. However, there is a problem in that visual features for the color image may be lost since color contrast between adjacent pixels on an image cannot be preserved. For example, when areas adjacent to one another in an original color image have different colors and the same luminance, clear distinction between these areas is found on the original color image, but no clear distinction between them is found on a converted grayscale image. Due to this, the grayscale image converted using luminance may have a different visual appearance than the original color image.
In order to solve this problem, color to grayscale image conversion methods for preserving color contrast of an original color image have been proposed, which may be largely divided into a global mapping method and a local mapping method.
According to the global mapping method, the same color is mapped into the same grayscale value to thereby generate a grayscale image. On the other hand, according to the local mapping method, even the same color is mapped into different grayscale values depending on positions in which pixels are placed on an image to thereby generate a grayscale image.
The above-mentioned conventional color to grayscale image conversion methods help preserve color contrast of an original color image in the converted grayscale image, but are limited in many practical applications.
For example, papers entitled, “Re-coloring Images for Gamuts of Lower Dimension,” by Rasche, et al., and “Color2Gray: Saliency Preserving Color Removal,” by Gooch, et al., disclose global mapping methods which have problems of taking more than several minutes to perform conversion for one image, that is, a slow processing speed. Additionally, many generated grayscale images have a different appearance than their original color images since no consideration is given for luminance of a color.
Also, a paper entitled, “Decolorize: fast, contrast enhancing, color to grayscale conversion,” by Grundland and Dodgson, discloses a global mapping method which has an advantage of a fast processing speed, but has a disadvantage in that excessively simple modeling of a global mapping function into a linear function leads to ineffective preservation of color contrast.
Also, papers entitled, “Spatial Color-to-Grayscale Transform Preserving Chrominance Edge Information,” by Bala and Eschbach, and “Apparent Grayscale: A Simple and Fast Conversion to Perceptually Accurate Images and Video,” by Smith, et al., disclose a color to grayscale image conversion method, classified as a local mapping method, in which areas with the same color may be mapped into different grayscale values, resulting in noticeable distortion of flat regions in the original color image.
As described above, the previous color to grayscale image conversion methods can help preserve color contrast of an original color image, but either involve high computational overhead or generate grayscale images having dissimilar visual appearance to the original color images.